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A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty

  • S.I. : Design and Management of Humanitarian Supply Chains
  • Published:
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Abstract

In the aftermath of a hurricane, humanitarian logistics plays a critical role in delivering relief items to the affected areas in a timely fashion. This paper proposes a novel stochastic look-ahead framework that implements a two-stage stochastic programming model in a rolling horizon approach to address the evolving uncertain logistics system state during the post-hurricane humanitarian logistics operations. The two-stage stochastic programming model that executes in this rolling horizon approach is formulated as a mixed-integer programming problem. The model aims to minimize the total cost incurred in the logistics operations, which consist of transportation cost and social cost. The social cost is measured as a function of deprivation for unsatisfied demand. Our extensive numerical results and sensitivity analysis demonstrate the effectiveness of the proposed approach in reducing the total cost incurred during the post-hurricane relief logistics operations compared to the two-stage stochastic programming model implemented in a static fashion.

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Notes

  1. Palmetto Cluster is Clemson University’s primary high-performance computing (HPC) resource, which is utilized by researchers, students, faculty, and staff from a broad range of disciplines.

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Acknowledgements

We would like to acknowledge the coordinating editor and two anonymous referees for their constructive suggestions that considerably improved the original version of this article. We also acknowledge partial support by the United States Department of Agriculture (USDA) under Grant 19-JV-11111133-037. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the USDA.

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Appendix: The static two-stage stochastic programming formulation for the post-hurricane relief logistics planning problem

Appendix: The static two-stage stochastic programming formulation for the post-hurricane relief logistics planning problem

$$\begin{aligned}&{\mathop {\min }} \quad z=\sum \limits _{t\in \mathcal {T}} \Big ( \sum \limits _{i\in \mathcal {L}}\eta _{i}y_{it} + \sum \limits _{i\in \mathcal {L}}\sum \limits _{k\in \mathcal {K}} \zeta _{k}V_{ikt} \Big ) \nonumber \\&\quad + \sum \limits _{\omega \in \Omega } P^{\omega } \sum \limits _{k\in \mathcal {K}}\sum \limits _{t\in \mathcal {T}} \Big (\sum \limits _{i\in L} B^{g}_{i} g^{\omega }_{ikt} + \sum \limits _{i\in \mathcal {L}\cup \mathcal {S}} B^{h}_{i} h^{\omega }_{ikt} + \sum \limits _{i\in \mathcal {L}\cup \mathcal {S}}\sum \limits _{j\in \mathcal {L}\cup \mathcal {S}, j\ne i} B_{ij} f^{\omega }_{ijkt}\Big )\nonumber \\&\quad +\sum \limits _{\omega \in \Omega } P^{\omega } \Big (\sum \limits _{i\in \mathcal {S}}\sum \limits _{k\in \mathcal {K}}\sum \limits _{t\in \mathcal {T}{\setminus }\{0\}}dep[w, i, k, U_{ik(t-1)}, t-1]\alpha _{ikt} \nonumber \\&\quad +\sum \limits _{i\in \mathcal {S}}\sum \limits _{k\in \mathcal {K}}dep[w, i, k, U_{ikT}, T]\Big ) \end{aligned}$$
(4a)
$$\begin{aligned}&\hbox {s.t.}\nonumber \\&V_{ik0}, U_{ik0}{= 0, \forall i\in \mathcal {S},k \in \mathcal {K}}{} \end{aligned}$$
(4b)
$$\begin{aligned}&V_{ik0}{= g^{\omega }_{ik0}, \quad \forall i\in \mathcal {L}, k \in \mathcal {K}, \omega \in \Omega }{} \end{aligned}$$
(4c)
$$\begin{aligned}&h^{\omega }_{ik0}{= 0, \quad \forall i\in \mathcal {L}\cup S, k \in \mathcal {K}, \omega \in \Omega }{} \end{aligned}$$
(4d)
$$\begin{aligned}&\sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S}, j \ne i }}f^{\omega }_{ijk0}{= 0, \quad \forall i \in \mathcal {L} \cup \mathcal {S}, k \in \mathcal {K}, \omega \in \Omega }{} \end{aligned}$$
(4e)
$$\begin{aligned}&x_{it}{= \sum ^{t}_{t^{'} = 0}y_{it^{'}}, \quad \forall i\in \mathcal {L}, t\in \mathcal {T}}{} \end{aligned}$$
(4f)
$$\begin{aligned}&{\sum _{t\in \mathcal {T}}y_{it}}{\le 1, \quad \forall i\in \mathcal {L}}{} \end{aligned}$$
(4g)
$$\begin{aligned}&{V_{ikt}}{\le \phi _{ik} x_{it}, \quad \forall i\in \mathcal {L}, k \in \mathcal {K}, t\in \mathcal {T}}{} \end{aligned}$$
(4h)
$$\begin{aligned}&{g^{\omega }_{ikt} + h^{\omega }_{ikt}}{\le V_{ikt}, \quad \forall i\in \mathcal {L}, k \in \mathcal {K}, t\in \mathcal {T}, \omega \in \Omega }{} \end{aligned}$$
(4i)
$$\begin{aligned}&{\sum \limits _{i\in \mathcal {L}}g^{\omega }_{ikt}}{\le R^{\omega }_{kt}, \quad \forall k \in \mathcal {K}, t\in \mathcal {T}, \omega \in \Omega }{} \end{aligned}$$
(4j)
$$\begin{aligned}&{V_{ikt}+ \sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S}, j \ne i}}f^{\omega }_{ijkt}}{= V_{ik(t-1)}+ g^{\omega }_{ikt} + h^{\omega }_{ikt}} \nonumber \\&\quad + \sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S}, j \ne i}}f^{\omega }_{jikt}, \quad \forall i \in \mathcal {L}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}, \omega \in \Omega \end{aligned}$$
(4k)
$$\begin{aligned}&{U_{ikt}}{= (1 - \alpha _{ikt})(U_{ik(t-1)}+1), \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}}{} \end{aligned}$$
(4l)
$$\begin{aligned}&{V_{ikt}}{\le \alpha _{ikt} \phi _{ik}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}}{} \end{aligned}$$
(4m)
$$\begin{aligned}&{\sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S}, j \ne i }}f^{\omega }_{ijkt}}{\le V_{ikt}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}, \omega \in \Omega }{} \end{aligned}$$
(4n)
$$\begin{aligned}&{\sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S}, j \ne i }}f^{\omega }_{jikt}}{\le V_{ikt}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}, \omega \in \Omega }{} \end{aligned}$$
(4o)
$$\begin{aligned}&{h^{\omega }_{ikt}}{\le V_{ikt}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}, \omega \in \Omega }{} \end{aligned}$$
(4p)
$$\begin{aligned}&{V_{ikt} + \sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S} \cup \{n\}, j \ne i}}f^{\omega }_{ijkt}}{=V_{ik(t-1)}- \alpha _{ikt} D^{\omega }_{ikt} + h^{\omega }_{ikt}} \nonumber \\&\quad + \sum \limits _{{\scriptscriptstyle j\in \mathcal {L} \cup \mathcal {S}, j \ne i}}f^{\omega }_{jikt}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}, \omega \in \Omega \end{aligned}$$
(4q)
$$\begin{aligned}&{x_{it},y_{it}}{\in \{0,1\}, \quad \forall i\in \mathcal {L}, t\in \mathcal {T}}{} \end{aligned}$$
(4r)
$$\begin{aligned}&{\alpha _{ikt}}{\in \{0,1\}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}{\setminus }\{0\}}{} \end{aligned}$$
(4s)
$$\begin{aligned}&{U_{ikt}}{\in \mathbb {Z^+}, \quad \forall i \in \mathcal {S}, k \in \mathcal {K}, t\in \mathcal {T}}{} \end{aligned}$$
(4t)
$$\begin{aligned}&{V_{ikt}}{\ge 0, \quad \forall i \in \mathcal {L} \cup S, k \in \mathcal {K}, t\in \mathcal {T}}{} \end{aligned}$$
(4u)
$$\begin{aligned}&{g^{\omega }_{ikt}, h^{\omega }_{ikt}}{\ge 0, \quad \forall i \in \mathcal {L}, k \in \mathcal {K}, t\in \mathcal {T}, \omega \in \Omega }{} \end{aligned}$$
(4v)
$$\begin{aligned}&{f^{\omega }_{ijkt}}\ge 0, \quad \forall i, j\in \mathcal {L}\cup \mathcal {S} \cup \{n\}, k \in \mathcal {K},\nonumber \\&t\in \mathcal {T}, \omega \in \Omega \end{aligned}$$
(4w)

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Chang, Y., Song, Y. & Eksioglu, B. A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty. Ann Oper Res 319, 1231–1263 (2022). https://doi.org/10.1007/s10479-021-04025-z

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